Neural networks are computing systems inspired by the biological neural networks that constitute animal brains. These powerful algorithms are at the core of many modern artificial intelligence applications, enabling machines to learn from data, recognize patterns, and make decisions with minimal human intervention. Their importance stems from their ability to model complex, non-linear relationships, surpassing traditional programming methods in tasks like image recognition, natural language processing, and predictive analytics. Research into deep learning, a subset of machine learning utilizing multi-layered neural networks, continues to drive innovation across diverse fields including healthcare, finance, and autonomous systems. Related concepts such as backpropagation, activation functions, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and artificial neural networks (ANNs) are frequently explored alongside core neural networks principles. The study of these systems is crucial for understanding and developing the next generation of intelligent technologies. In this section of our website, we provide a comprehensive library featuring the latest graduation theses, master’s dissertations, and doctoral theses covering neural networks, available for download in PDF format.